feat(applications): submit Google Sr Data Engineer + Kraken SRE AI Agents (2026-06-15)

Two applications sent and finalized on 2026-06-15:

- Google - Senior Data Engineer (Merchant Data Science, Zurich), 85.5/100.
  Tier-1 scope fix + both Tier-2 polish applied: re-scoped the Swisscom
  migration claim in resume B2 + CL P2 (Scope-Discipline), added project-
  delivery vocab (B4), and JD-exact 'distributed data processing' (B5).
- Kraken (Payward) - SRE, AI Agents (remote, CH-eligible), 87.2/100.
  Finalized as-is; crypto-native + production-ML edge, honest infra gaps.

Logs both as 'applied' in job_scout/state/decisions.json and flips their
CLAUDE.md Active Sessions rows to SENT. Open item for both: confirm level
and comp clear the 180k+ all-in bar at the recruiter stage.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

output/Google_Senior_Data_Engineer/JD_google_senior_data_engineer.txt

@@ -0,0 +1,46 @@
+Senior Data Engineer — Google (Merchant Data Science, Merchant Shopping Organization)
+
+JD source: live scrape 2026-06-15 via Playwright (Google careers board), re-verified live same day
+URL: https://www.google.com/about/careers/applications/jobs/results/87066954308690630-senior-data-engineer?location=Switzerland
+Location: Mountain View, CA, USA; Zürich, Switzerland (preferred-location choice at apply)
+Level chip: "Mid" (title says Senior — clarify L4 vs L5 at recruiter stage)
+Comp (US band shown): $156,000 - $227,000 USD + 15% bonus target + equity + benefits. Zürich band NOT posted — verify clears 180k+ all-in.
+
+--- VERBATIM POSTING TEXT ---
+
+Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Mountain View, CA, USA; Zürich, Switzerland.
+
+Minimum qualifications:
+Bachelor's degree or equivalent practical experience.
+5 years of experience designing data pipelines, and dimensional data modeling for synch and asynch system integration and implementation using internal (e.g., Flume, etc.) and external stacks (DataFlow, Spark, etc.).
+
+5 years of experience coding in one or more programming languages.
+
+5 years of experience working with data infrastructure and data models by performing exploratory queries and scripts.
+
+Preferred qualifications:
+5 years of experience with statistical methodology and data consumption tools such as business intelligence tools, collabs, jupyter notebooks, Tableau, Power BI, DataStudio, and business intelligence platforms.
+
+3 years of experience developing project plans and delivering projects on time within budget and scope.
+
+3 years of experience partnering with stakeholders (e.g., users, partners, customer), and managing stakeholders/customers.
+
+Experience with Machine Learning for production workflows.
+
+About the job
+The Merchant Data Science team is a group within the Merchant Shopping Organization. We work on building scalable data products that empower data-driven decision-making.
+
+In this role, you will innovate and build durable, impactful data products. You will bridge the gap between software engineering, data engineering, and data science.
+
+As a Data Engineer in the Merchant Shopping organization, you will build data products and foundations to improve Google's Shopping products. You will collaborate with a multidisciplinary team of data scientists, engineers, and PMs on a wide range of problems. You will bring an understanding of data, logging, and engineering. You will solve non-routine problems, build reliable data products used across the organization, and drive impact on cross-functional projects.
+
+Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
+
+US: $156000 - $227000 (USD) + 15% bonus target + equity + benefits
+
+Responsibilities
+Identify the underlying need, process datasets, and apply advanced data engineering, data modeling, and architectural frameworks when needed.
+Design, build, and scale innovative data products, including self-serve tools, and automated pipelines.
+Advance data infrastructure, product quality, and foundational understanding through automated validation frameworks, data quality, and reliability monitoring.
+Operate with a high degree of autonomy, owning data engineering projects from initial conception to landing and impact.
+Advocate impactful data products while contributing to a team culture that values engineering excellence, robust data, and sharp communication.

output/Google_Senior_Data_Engineer/critique_google_senior_data_engineer.md

@@ -0,0 +1,220 @@
+# Critique: Google — Senior Data Engineer (Merchant Data Science, Merchant Shopping) — Zürich
+
+**Resume:** `output/Google_Senior_Data_Engineer/e2e_google_senior_data_engineer_resume.tex`
+**Cover Letter:** `output/Google_Senior_Data_Engineer/e2e_google_senior_data_engineer_cover_letter.tex`
+**JD source:** live scrape 2026-06-15 via Playwright (real Google posting, verbatim) — JD integrity OK
+**Date:** 2026-06-15
+**Score: 85.5 / 100** (Tier-1 scope fix + both Tier-2 items APPLIED 2026-06-15; baseline pre-edit was 83.0)
+
+> **Edits applied 2026-06-15 (this critique pass):**
+> - **Tier 1 (scope/accuracy):** resume B2 + CL P2 migration claim re-scoped — "Migrated Swisscom's legacy Teradata/Oracle ETL" → "Migrated my Fulfillment and Product Analysis ETL from Teradata/Oracle to Swisscom's … AWS platform" (resume) / "As Swisscom moved to a cloud-native AWS platform …, I migrated my Fulfillment and Product Analysis ETL off Teradata and Oracle" (CL). Recurring flagged Scope-Discipline error cleared in both docs.
+> - **Tier 2.1:** resume B4 now hits the project-delivery preferred qual — "for B2B stakeholder teams **on time and in scope**" (dropped "3rd-level root cause analysis" for space).
+> - **Tier 2.2:** resume B5 "distributed computing" → "distributed data processing" (JD's exact phrase).
+> - Re-verified: resume 2pp / CL 1pp, clean compile, no orphans; B2 216 / B4 190 / B5 206 chars (all ≤218 max); AI scan still clean.
+> Remaining gap is the structural hard ceiling (~87): no GCP/BigQuery-by-name, no marketplace domain — not truthfully closable.
+
+---
+
+## Domain-Specialist Lens
+
+### Reviewer Persona
+A Google L5/L6 Data Engineer tech lead or DE manager on Merchant Data Science (Zürich). Reads pipelines + dimensional-modeling depth, data-product thinking (self-serve, durable, consumed at scale), data-quality/validation rigor, and autonomy. Has screened dozens of DEs with BigQuery/Dataflow/dbt + marketplace data. Rolls eyes at BI-dashboard-only profiles and "moved data from A to B" with no modeling. Genuinely interested by someone who has *designed data models and owned data products others depend on*, not just run ETL.
+
+### Company Context
+Merchant Shopping Org; the Shopping Graph indexes 60B+ product listings (confirmed Google I/O, 19 May 2026). Merchant Data Science builds scalable data products powering data-driven decisions + AI Shopping experiences (Gemini/AIM). Role explicitly "bridges SWE, DE, and DS." Insider vocabulary: data products, self-serve, dimensional modeling, fact/dim tables, data quality SLAs, validation frameworks, freshness/lineage, Flume/Dataflow/BigQuery, batch+streaming, idempotency, backfill. "Equivalent practical experience" explicitly accepted in min quals.
+
+### JD Vocabulary Extraction (ranked)
+| # | JD Term | Freq | Meaning here | Resume Match? |
+|---|---|---|---|---|
+| 1 | data products (durable, self-serve) | High (title-adjacent, 3×) | Reusable assets other teams consume without re-engineering | YES — near-verbatim, flagship (SW-7) |
+| 2 | designing data pipelines | High (min qual) | Production pipeline design, not maintenance | YES |
+| 3 | dimensional data modeling | High (min qual) | Fact/dim schema design | YES — verbatim, 3× |
+| 4 | data infrastructure / data models | High (min qual) | Platform + exploratory queries | YES |
+| 5 | Flume / DataFlow / Spark | Med (min qual) | Internal + external distributed stacks | PARTIAL — Spark direct; Flume/DataFlow honest bridge (CL), never claimed |
+| 6 | automated validation / data quality / reliability monitoring | High (resp) | Quality frameworks, freshness/anomaly | YES (BS-4) |
+| 7 | autonomy, conception→landing→impact | High (resp) | Owns projects end-to-end | YES — Component/App Owner |
+| 8 | BI / data-consumption tools, statistical methodology | Med (pref) | Tableau/PowerBI/DataStudio/Jupyter | PARTIAL — Spotfire/Tableau/QuickSight/Jupyter; no PowerBI/DataStudio |
+| 9 | ML for production workflows | Med (pref) | ML in prod | YES (BS-1) |
+| 10 | stakeholder partnering, project delivery on time/budget/scope | Med (pref) | Cross-functional + PM discipline | YES on stakeholders; THIN on explicit "on time/budget/scope" phrasing |
+
+### Domain Vocabulary Map
+| Resume currently says | Could say for this JD | Why |
+|---|---|---|
+| "Migrated Swisscom's legacy Teradata/Oracle ETL" | "Migrated my Fulfillment/Product Analysis ETL onto Swisscom's AWS platform" | Scope the object — see Tier 1 (accuracy) |
+| "distributed computing" (B5) | "distributed data processing" | JD's exact phrase; tiny swap |
+| (implicit delivery) | "delivered on time, within scope" somewhere | Hits the 3-yr project-delivery preferred qual verbatim |
+
+### Gap Ranking
+- **Fatal:** None. Every minimum qual is met (pipelines, dimensional modeling, 5+ yrs coding, data infra) and Google explicitly accepts equivalent practical experience.
+- **Serious:** No BigQuery/Dataflow/Flume hands-on by name; no Google-scale or e-commerce/marketplace domain. These are real and **correctly not faked** — handled as honest bridges. They cap the ceiling but won't trigger ATS/HR rejection.
+- **Cosmetic:** No PowerBI/DataStudio (has Tableau/Spotfire/QuickSight — equivalent); explicit "on time/budget/scope" phrasing absent; "synch/asynch" not verbatim.
+
+### Methodology Transfer Test (top 5 achievements)
+| Achievement | How a Google Merchant-DS expert reads it |
+|---|---|
+| SW-7 self-serve governed data products on Data Mesh | "This is literally our job description — durable data products consumed across teams." ✅ Strongest transfer in the package. |
+| SW-1 Teradata/Oracle→AWS migration + dimensional schemas | "Cloud warehouse migration + dimensional modeling — maps to our BigQuery/Dataflow world conceptually." ✅ (verb-scope caveat below) |
+| SW-6 PySpark distributed processing | "Spark transfers to Dataflow/Flume; he says so honestly." ✅ |
+| BS-4 anomaly detection + data-quality stack | "Validation frameworks + reliability monitoring — exactly our 'automated validation' responsibility." ✅ |
+| BS-1 production ML inference in 24/7 fab | "ML for production workflows preferred qual, in a no-downtime environment." ✅ |
+
+All five transfer naturally and honestly. This is a genuine Tier-1 fit, not a reframe stretch.
+
+### Competitive Landscape
+- **Obvious fit:** ex-FAANG or marketplace DE fluent in BigQuery/Dataflow/dbt, dimensional modeling at Google scale, e-commerce data.
+- **Our advantage:** self-serve data-product / Data-Mesh platform thinking at telco scale (the JD's #1 ask, near-verbatim), production ML, data-quality/reliability discipline, broad multi-industry delivery, local to Zürich.
+- **Their advantage:** GCP-by-name, Google-scale, marketplace domain. Unbridgeable truthfully → defines the hard ceiling.
+
+---
+
+## Five-Perspective Read-Through
+
+### ATS Robot
+Verbatim/semantic matches: data pipelines ✅, dimensional data modeling ✅, data products ✅, self-serve tools ✅, automated pipelines ✅, data infrastructure ✅, data models ✅, Spark/PySpark ✅, Airflow ✅, SQL ✅, Python ✅, Kafka ✅, data quality ✅, automated validation ✅, reliability monitoring ✅, stakeholder ✅, ML production ✅, Jupyter ✅, Tableau ✅, statistical methods ✅. Missing: Flume ❌ (intentional), DataFlow ❌ (intentional, bridged in CL), DataStudio ❌, Power BI ❌, explicit "project plans/on time/budget/scope" ❌.
+**Match rate: ~18/22 ≈ 82% → PASS.** No high-frequency JD term sits at 0 in the resume except the GCP-internal tools (correctly avoided).
+
+### Recruiter Glance (10s)
+**Verdict: FORWARD (80%).** Staff Data Engineer at Switzerland's largest telco + AWS SAA + a tagline in exact target vocabulary ("Data Products, Dimensional Modeling & Pipeline Ownership"). Credible at level instantly. Only drag: Google's bar is brutal and there's no FAANG/GCP name on the page.
+
+### HR Screen (30s)
+**Verdict: PHONE SCREEN (80%).** Summary bridges cleanly ("designing and owning production data pipelines, dimensional data models and self-service data products"). 11+ years clears the 5-yr bars several times over. Skills group names signal target domain. Equivalent-practical-experience clause covers the degree.
+
+### Hiring Manager (2min)
+**Verdict: INTERVIEW / leaning yes (60–65%).**
+Top 3 observations:
+1. SW-7 self-serve governed data products = the team's charter almost verbatim — immediate "this person already does our job."
+2. Honest about the GCP gap (CL: "carries over to Google's Flume and Dataflow stack, though not by name") — reads as a confident engineer, not a buzzword-stuffer.
+3. Would notice the absence of any Google-scale / marketplace data point, and would probe dimensional-modeling depth.
+**Predicted first interview question:** "Walk me through the dimensional model behind one of your self-serve data products — grain, fact/dim split, and how you handle late-arriving data and backfills."
+
+### Technical Reviewer (10min)
+**Truthfulness:** One scope overclaim (see Tier 1) in resume B2 and CL P2: "Migrated Swisscom's legacy Teradata/Oracle ETL" reads as a solo claim on a company-wide migration that Dennis contributed to within his domains (per CLAUDE.md Scope Discipline + `[[feedback_swisscom_datamesh_ownership]]`). Everything else verified: AWS SAA active to Sep 2027 ✅, no LangChain ✅, crypto dropped ✅, no Security Champion ✅, Generali=Hamburg ✅, Bosch=Dresden ✅, languages German/English only ✅, TAF 2022 talk reserved for CL (verified) ✅, no LOC/test counts ✅.
+**Consistency:** Resume ↔ CL aligned (same stack, same titles, same hooks). The B2/P2 scope issue appears in *both* — fix in both.
+
+---
+
+## Eight-Dimension Scoring
+
+| Dimension | Score | Weight | Weighted | Notes |
+|---|---|---|---|---|
+| ATS Keywords | 8.5/10 | 15% | 1.275 | ~82% match; honest GCP-tool gaps the only zeros |
+| Summary | 8.5/10 | 10% | 0.85 | Strong bridge, leads with data products; no headline metric (typical for DE) |
+| Skills Section | 8.5/10 | 10% | 0.85 | BI/Analytics group well-added; all relevant; bolds accurate |
+| Bullet Quality | 8.2/10 | 25% | 2.05 | Excellent reframing + near-verbatim SW-7; docked for B2 scope overclaim |
+| Pubs / Credentials | 8.0/10 | 10% | 0.80 | No pubs (expected); AWS SAA + Data Eng + IBM AI Eng + iSAQB are solid signals |
+| Narrative Coherence | 8.5/10 | 15% | 1.275 | Clean data-product → quality → ML → delivery arc; consistent thread |
+| Page Fill & Visual | 8.5/10 | 5% | 0.425 | Clean compile, 2pp, no orphans; page 2 has modest lower slack (acceptable) |
+| Credibility Signals | 8.0/10 | 10% | 0.80 | Component/App Owner titles, AWS cert, CNN/BBC; scope overclaim slightly dents under expert read |
+| **Total** | | **100%** | **83.3** | Rounded **83.0** |
+
+---
+
+## Interview Likelihood
+
+| Reader | Probability | Key Factor |
+|--------|------------|-----------|
+| ATS | 90% PASS | Core DE keywords all present verbatim |
+| Recruiter (10s) | 80% FORWARD | Staff title + AWS cert + on-target tagline |
+| HR (30s) | 80% PHONE SCREEN | Clears every min qual; equivalent-experience clause |
+| Hiring Manager (2m) | 62% INTERVIEW | SW-7 = team charter; offset by no GCP/marketplace name |
+| Technical Panel (10m) | YES with probes | Will test dimensional-modeling depth + Spark→Dataflow transfer |
+
+**Ceiling:** Current 83 → +Tier 1 scope fix ≈ 85.5 → theoretical max (this candidate+JD) ≈ 86–87. **Hard ceiling ~87**, set by no GCP/BigQuery-by-name and no Google-scale/marketplace domain — not truthfully closable. What would close it: a hands-on BigQuery/Dataflow project or e-commerce/marketplace data work (neither exists; do not fabricate).
+
+---
+
+## Actionable Improvements
+
+### Tier 1 (HIGH — do this one)
+1. **Scope the migration claim (accuracy — recurring flagged error). +~2 pts.** Appears in **both** resume B2 and CL P2.
+   - Resume B2 — current: *"Migrated Swisscom's legacy Teradata/Oracle ETL to a cloud-native AWS platform (S3, Glue, Athena/Iceberg, Redshift, Airflow), modeling dimensional schemas..."*
+   - → Proposed: *"Migrated my Fulfillment and Product Analysis ETL off Teradata/Oracle onto Swisscom's cloud-native AWS platform (S3, Glue, Athena/Iceberg, Redshift, Airflow), modeling the dimensional schemas..."*
+   - CL P2 — current: *"I migrated Swisscom's legacy Teradata and Oracle ETL onto a cloud-native AWS platform..."*
+   - → Proposed: *"As Swisscom moved to a cloud-native AWS platform (Glue, Athena/Iceberg, Redshift, Airflow), I migrated my Fulfillment and Product Analysis ETL off Teradata and Oracle and modeled the dimensional schemas behind it..."*
+   - **Why:** "Migrated Swisscom's legacy … ETL" pairs a full-ownership verb with a company-wide object — the exact pattern banned in CLAUDE.md Scope Discipline and `[[feedback_swisscom_datamesh_ownership]]` (the ODP migration is company-wide, Dennis contributed within his domains). Scoping the object keeps every ATS keyword and reads *more* credible to an L5 DE, not less.
+
+### Tier 2 (MEDIUM — optional)
+1. **Add explicit project-delivery phrasing (~+0.4).** The 3-yr "developing project plans and delivering on time, within budget and scope" preferred qual has no verbatim hook. If a bullet can absorb it (e.g., fold "delivered on time and in scope" into B3 or the Bosch App-Owner bullet), it's a cheap ATS+HR win. Watch char limits — both candidate bullets are already near target.
+2. **"distributed computing" → "distributed data processing" in B5 (~+0.2).** JD's exact phrase; one-word-ish swap, no length change.
+
+### Tier 3 (COSMETIC — skip)
+1. "synch/asynch" verbatim insertion — Kafka+batch already covers it semantically; not worth the bullet space.
+2. Page-2 lower slack — within tolerance for a 2-page resume; do not pad.
+
+### Verdict
+Apply Tier 1 (it's an accuracy fix on a recurring flagged error, in both documents). Tier 2 are genuine but optional polish. Tier 3 skip.
+
+---
+
+## Interview Bridge Points
+
+| Resume Topic | Target Equivalent | Opening Line |
+|---|---|---|
+| SW-7 self-serve data products | Merchant Data Science's durable data products | "The self-serve data products I own at Swisscom are the same primitive your team builds — assets other teams query without coming back to me; the difference is scale, not concept." |
+| SW-6 PySpark / Data Lake | Flume / Dataflow | "My PySpark batch+streaming work maps directly onto Flume and Dataflow — same distributed-processing model, columnar lake underneath; I'd ramp on the API, not the paradigm." |
+| SW-1 dimensional modeling | BigQuery fact/dim schemas | "I model fact/dimension schemas on Athena/Iceberg/Redshift today; ZetaSQL/BigQuery is a dialect and storage shift, not a modeling one." |
+| BS-4 anomaly detection + data quality | Automated validation frameworks | "Reliability monitoring for a 24/7 fab is the same discipline as your automated validation — freshness, completeness, anomaly alerting with no maintenance window." |
+| BS-1 production ML inference | ML for production workflows | "I containerized ML inference into a fab with zero downtime tolerance — the operational bar for production ML is something I've already lived." |
+| BS-5 Spotfire + TAF 2022 talk | Data consumption / how analysts use products | "I co-owned a Spotfire analytics platform and spoke at TIBCO Analytics Forum 2022 on spatial-variability visualization — I think about the consumption end, not just the pipeline end." |
+| Component/Application Owner | Autonomy, conception→landing→impact | "I've held formal Component and Application Owner titles — I own things end to end with on-call accountability, which is exactly the autonomy this role describes." |
+
+---
+
+## Cover Letter Critique
+
+**Institution type:** Industry (big-tech, DE audience). **Word count:** 299 — within the 250–300 industry target. ✅
+
+### 6A. Anti-Pattern Checklist
+- [x] Opens with a specific fact (60B Shopping Graph), not "I am writing to express…"
+- [x] Adds narrative context, doesn't rehash bullets in prose
+- [x] Names specific product/program (Shopping Graph, Merchant Data Science, Flume/Dataflow, TAF 2022)
+- [x] Clear "why this team" (Zürich, bridge SWE/DE/DS, local from Bern)
+- [x] Strongest qual (self-serve data products) in P1
+- [x] No apologetic gap language — gap stated confidently ("though not by name")
+- [x] Active close ("I'd welcome a conversation about the team's roadmap")
+- [x] Credentials woven into body, not dumped
+
+### 6B. Tailoring Signals
+Names Shopping Graph + Merchant Data Science + Flume/Dataflow; uses ≥3 supplementing JD terms (durable/reusable data products, dimensional modeling, autonomy); references mission (data foundation for AI Shopping); proposes a concrete method↔need connection. ✅
+
+### 6C. Industry-Specific
+- [x] Business-value translation present ("decisions merchants and shoppers act on")
+- [x] No "leaving academia" framing
+- [x] Jargon calibrated to a DE reader
+
+### 6D. CL ATS
+Supplementing keywords present: Shopping Graph, self-service/durable data products, dimensional modeling, PySpark, Flume/Dataflow, ELK/Grafana/Prometheus, production ML, Component Owner. 7+ — strong.
+
+### 6E / 6F. Structural & Cohesion
+- [x] Claims match resume; no new unsupported achievements
+- [x] CL deepens (motivation, "one scale down," consumption angle), doesn't restate
+- [x] Word count 299; tone results-driven
+- [x] Quantification present (60B listings, 24/7, 300mm, TAF 2022) — 4+, not a fact sheet
+- [⚠] **One contradiction to fix alongside Tier 1:** CL P2 "I migrated Swisscom's legacy Teradata and Oracle ETL" carries the same scope overclaim as resume B2 — fix in both so the documents stay consistent.
+
+### 6G. AI Fingerprint Scan (12-item)
+- Tier-1 banned words: none. ✅
+- Banned phrases: none. ✅
+- Em-dashes: **0** in CL (commas used for the appositive), ≤2 in resume (en-dashes only). ✅
+- Bullet -ing analysis endings: none. ✅
+- 3+ consecutive same-length sentences: no — CL mixes short ("That is the work I do today, one scale down.") with long. ✅
+- Repeated paragraph-start structure: no (P1 "Google's…", P2 "Most of…", P3 "Two things…"). ✅
+- >2 triplet structures: borderline (two "X, Y, and Z"; one quotes the JD) — acceptable. ✅
+- Generic opener: no. ✅
+- Metaphorical landscape/journey/realm: none. ✅
+- Passive-voice bullets >20%: no. ✅
+**AI scan: clean.**
+
+---
+
+## Part 7: Post-Generation Verification
+
+**Mechanical:** All 17 variable bullets within 189–218 char range ✅ (char_count.py: bullets 1–17 OK/NEAR-MAX, none OVER). Cert lines 1L "SHORT" — FIXED section, acceptable. No orphans ✅. Resume 2 pages ✅. CL 1 page, 299 words ✅. One 0.97pt overfull hbox in CL — sub-pixel, invisible.
+**Content:** ATS ≥70% ✅. Provenance clean ✅. No forbidden terms (no LangChain, no LOC/test counts, no crypto, no Security Champion) ✅. No publication entries to mismatch. CL claims traceable to resume ✅.
+**Structural:** "Google"/"Swisscom"/"Bosch"/"Generali"(Hamburg)/"Fraunhofer"/"Vizrt" all correct ✅. Complete preambles, compiles standalone ✅. Dates "Mon YYYY -- Mon YYYY" ✅. Email dennis@thiessen.io ✅. No US immigration line (Zürich target) ✅.
+
+**Only flag rising to Tier 1:** the migration scope overclaim (resume B2 + CL P2).
+
+---
+
+*End of critique.*

output/Google_Senior_Data_Engineer/e2e_google_senior_data_engineer_cover_letter.tex

@@ -0,0 +1,44 @@
+\documentclass[11pt,a4paper,roman]{moderncv}
+\usepackage[english]{babel}
+\moderncvstyle{classic}
+\moderncvcolor{green}
+\usepackage[utf8]{inputenc}
+\usepackage[T1]{fontenc}
+\usepackage{ragged2e}
+\usepackage[scale=0.80]{geometry}
+\usepackage[version=4,arrows=pgf-filled]{mhchem}
+\renewcommand*{\makeletterclosing}{\par\vspace{2ex}\closingname\par}
+\microtypesetup{expansion=false}
+
+% ========== HEADER ==========
+\name{Dennis}{Thiessen, M.Eng.}
+\address{Bern, Switzerland}{}{}
+\phone[mobile]{+41~795~955~585}
+\email{dennis@thiessen.io}
+\extrainfo{\href{https://linkedin.com/in/dennis-thiessen}{linkedin.com/in/dennis-thiessen}}
+% ============================
+
+\begin{document}
+
+\recipient{Hiring Team}{Merchant Data Science, Merchant Shopping\\Google Switzerland GmbH\\Z\"urich, Switzerland}
+\date{\today}
+\opening{Dear Merchant Data Science Hiring Team,}
+\makelettertitle
+
+\begin{justify}
+
+Google's Shopping Graph now indexes over 60 billion product listings, and the Merchant Data Science team in Z\"urich builds the data foundation that turns that scale into decisions merchants and shoppers act on. That is the work I do today, one scale down. At Swisscom, Switzerland's largest telco, I design and own self-service, governed data products that analytics and engineering teams depend on. The role's explicit aim, bridging software engineering, data engineering, and data science, is how I already work as a Component Owner. Based in Bern, I'd be local to the Z\"urich team.
+
+Most of my last decade has been pipeline design and dimensional modeling in production. As Swisscom moved to a cloud-native AWS platform (Glue, Athena/Iceberg, Redshift, Airflow), I migrated my Fulfillment and Product Analysis ETL off Teradata and Oracle, modeled the dimensional schemas behind it, and own the business-critical Fulfillment pipelines that feed it under an on-call SLA. On that platform I build the self-serve data products other teams query without coming back to me, the durable, reusable assets your posting describes. For heavy workloads I use PySpark across our Data Lake; that distributed-processing experience carries over to Google's Flume and Dataflow stack, though not by name.
+
+Two things round out the fit. Data quality and reliability are not afterthoughts for me: at Bosch I built the anomaly-detection and monitoring stack (ELK, Grafana, Prometheus) for 24/7 manufacturing data and moved production ML inference into a 300mm wafer fab, where there are no maintenance windows. I also care about how people consume data. I co-owned Bosch's TIBCO Spotfire analytics platform and spoke at the TIBCO Analytics Forum 2022 on visualizing spatial variability in semiconductor products. Building data products that AI-powered Shopping experiences and human analysts rely on is where I want to work next, and I'd welcome a conversation about the team's roadmap.
+
+\end{justify}
+
+\vspace{0.3cm}
+{Sincerely,\\
+Dennis Thiessen, M.Eng.\\
+Staff Data, Analytics \& AI Engineer\\
+Swisscom (Schweiz) AG}
+
+\end{document}

output/Google_Senior_Data_Engineer/e2e_google_senior_data_engineer_resume.tex

@@ -0,0 +1,162 @@
+\documentclass{resume}
+\usepackage{hyperref}
+\usepackage{enumitem}
+\usepackage{fontawesome}
+\usepackage{tikz}
+\usepackage{graphicx}
+\hypersetup{
+    colorlinks = true,
+    linkcolor = [rgb]{0.9,0.4,0.4},
+    anchorcolor = [rgb]{0.9,0.4,0.4},
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+    filecolor = [rgb]{0.4,0.4,0.4},
+    urlcolor = [rgb]{0.0,0.0,0.99},
+}
+\usepackage{xcolor}
+\usepackage[utf8]{inputenc}
+\usepackage[T1]{fontenc}
+\usepackage{lmodern}
+\usepackage[version=4,arrows=pgf-filled]{mhchem}
+\usepackage[includefoot,left=0.5in,top=0.5in,right=0.5in,bottom=0.2in,textwidth=7.5in,textheight=10.8in]{geometry}
+\usepackage{fancyhdr}
+\pagestyle{fancy}
+\fancyhf{}
+\renewcommand{\headrulewidth}{0pt}
+\fancyfoot[R]{\hfill \thepage/\pageref{LastPage}}
+\newcommand{\tab}[1]{\hspace{.2667\textwidth}\rlap{#1}}
+\newcommand{\itab}[1]{\hspace{0em}\rlap{#1}}
+
+%----------------------------------------------------------------------------------------
+%	HEADER
+%----------------------------------------------------------------------------------------
+\name{Dennis Thiessen, M.Eng.}
+\address{\href{https://linkedin.com/in/dennis-thiessen}{LinkedIn}}
+\address{dennis@thiessen.io \\ +41 795 955 585}
+\address{Bern, Switzerland $\vert$ German citizen (EU) $\vert$ Open to Z\"urich (on-site / hybrid)}
+\address{{Senior Data Engineer $\vert$ Python $\cdot$ SQL $\cdot$ AWS $\cdot$ Spark $\vert$ Data Products, Dimensional Modeling \& Pipeline Ownership}}
+
+
+\begin{document}
+
+\vspace{-0.15cm}
+
+%----------------------------------------------------------------------------------------
+%	SUMMARY
+%----------------------------------------------------------------------------------------
+\begin{rSection}{Summary}
+Senior data engineer with 11+ years designing and owning production data pipelines, dimensional data models and self-service data products. At Switzerland's largest telco I build governed, reusable \textbf{data products} on \textbf{AWS} (Glue, Athena/Iceberg, Redshift, \textbf{Airflow}) and own business-critical Oracle/\textbf{Kafka}-to-Teradata \textbf{ETL} under SLA. Earlier I co-owned a \textbf{TIBCO Spotfire} analytics platform and moved \textbf{ML} inference into a 24/7 Bosch fab. \textbf{AWS} Certified Solutions Architect; \textbf{Python} and \textbf{SQL} expert with \textbf{PySpark} for distributed processing.
+\end{rSection}
+\vspace{-0.15cm}
+
+%----------------------------------------------------------------------------------------
+%	TECHNICAL SKILLS — Format C, 5 groups (4-3-2-2-2)
+%----------------------------------------------------------------------------------------
+\begin{rSection}{Technical Skills}
+
+\begin{skillgroup}{Data Engineering \& Pipelines}
+\skilldash{ETL/ELT pipeline design, dimensional data modeling, data warehousing, \textbf{data products}, data governance}
+\skilldash{\textbf{Apache Airflow}, \textbf{Apache Kafka}, batch \& streaming ingestion, SAP BODS, metadata management}
+\skilldash{\textbf{PySpark} / \textbf{Apache Spark}, distributed data processing, Hadoop / Impala, large-scale batch}
+\skilldash{Data quality, automated validation, reliability monitoring (ELK, \textbf{Grafana}, \textbf{Prometheus}, Loki)}
+\end{skillgroup}
+
+\begin{skillgroup}{Cloud Platform \& Infrastructure}
+\skilldash{\textbf{AWS} (S3, Glue, Athena/Iceberg, Redshift, Lambda, Step Functions, CloudWatch) -- SAA-certified}
+\skilldash{Infrastructure as Code (CloudFormation), serverless \& event-driven architecture, ECR/ECS}
+\skilldash{\textbf{Kubernetes}, \textbf{Docker}, \textbf{GitLab CI/CD}, Jenkins, Ansible, Linux, Git}
+\end{skillgroup}
+
+\begin{skillgroup}{BI, Analytics \& Data Consumption}
+\skilldash{\textbf{TIBCO Spotfire} (C\# extensions, platform owner), \textbf{Tableau}, AWS QuickSight, dashboards \& reporting}
+\skilldash{Jupyter / Pandas, exploratory analysis, statistical methods, stakeholder analytics}
+\end{skillgroup}
+
+\begin{skillgroup}{Programming \& Databases}
+\skilldash{\textbf{Python} (expert), \textbf{SQL}, Java, C\#, Bash, JavaScript/TypeScript, FastAPI, pytest}
+\skilldash{Teradata, Oracle DB, Redshift, Athena, ImpalaSQL, relational \& dimensional modeling}
+\end{skillgroup}
+
+\begin{skillgroup}{Certifications}
+\skilldash{\textbf{AWS Certified Solutions Architect -- Associate} (active to Sep 2027), Data Engineering with AWS (Udacity)}
+\skilldash{iSAQB CPSA -- Foundation (2016), ITIL Foundation (2016), IBM AI Engineering Specialization}
+\end{skillgroup}
+
+\end{rSection}
+\vspace{-0.15cm}
+
+%----------------------------------------------------------------------------------------
+%	PROFESSIONAL EXPERIENCE
+%----------------------------------------------------------------------------------------
+\begin{rSection}{Professional Experience}
+
+% --- Swisscom (Oct 2023 -- Present) — SW-7, SW-1, SW-2, SW-4, SW-6, SW-3 ---
+\begin{rSubsection}{Self-Service Data Products, Dimensional Modeling \& Cloud-Native Pipelines}{\textcolor{black!60}{Oct 2023 -- Present}}{Staff Data, Analytics \& AI Engineer, Swisscom (Schweiz) AG}{Bern, Switzerland}
+\item Build governed, self-service \textbf{data products} with metadata management within Swisscom's \textbf{Data Mesh} on \textbf{AWS} (Glue, Athena, CloudFormation, CI/CD), consumed by downstream analytics and engineering teams.
+\item Migrated my Fulfillment and Product Analysis \textbf{ETL} from Teradata/Oracle to Swisscom's cloud-native \textbf{AWS} platform (Glue, Athena/Iceberg, Redshift, \textbf{Airflow}), modeling dimensional schemas for batch and analytics workloads.
+\item Own business-critical Fulfillment \textbf{ETL} pipelines (Oracle, \textbf{Kafka} to Teradata in \textbf{Python}) as Component Owner, enforcing dimensional data models, data governance and data quality under on-call SLA.
+\item Deliver data products, dashboards (\textbf{Tableau}, QuickSight) and analyses for B2B stakeholder teams on time and in scope, partnering with product owners and automating recurring \textbf{Python} workflows.
+\item Apply \textbf{PySpark} and distributed data processing in the Swisscom Data Lake, extending \textbf{Python} and \textbf{SQL} pipelines to large-scale batch and streaming workloads across Fulfillment and Product Analysis data domains.
+\item Design, deploy and operate \textbf{Python} data applications on \textbf{Kubernetes} with \textbf{GitLab CI/CD}, owning containerized delivery from build and test through production rollout and operation in an agile DevOps team.
+\end{rSubsection}
+
+% --- Bosch (Feb 2020 -- Dec 2022) — BS-1, BS-3+BS-5, BS-4, BS-2 ---
+\begin{rSubsection}{Production Data Engineering, ML \& Analytics Platform Ownership}{\textcolor{black!60}{Feb 2020 -- Dec 2022}}{(Senior) Data Engineer, Robert Bosch Semiconductor Manufacturing}{Dresden, Germany}
+\item Containerized and orchestrated production \textbf{ML} inference (\textbf{Docker}, \textbf{Kubernetes}, Ansible) for automated image-based defect classification in Bosch's 24/7 semiconductor fab across 300mm wafer lines.
+\item Co-owned the \textbf{TIBCO Spotfire} analytics platform and the Defect Management System as Application Owner, building C\# extensions and wafer-map visualizations, defining SLOs and training engineering users.
+\item Delivered an anomaly-detection and data-quality stack (ELK with \textbf{Kafka} on \textbf{Docker}, plus \textbf{Grafana}, \textbf{Prometheus}, Loki), giving centralized monitoring, validation and alerting for 24/7 manufacturing data.
+\item Developed data services in \textbf{Python}, Java and C\# over OracleDB and Hadoop/ImpalaSQL, giving analysis teams structured, reliable access to defect-management and process-optimization data in a high-throughput fab.
+\end{rSubsection}
+
+% --- Fraunhofer (Sep 2018 -- Oct 2019) — FC-1, FC-3 ---
+\begin{rSubsection}{Data-Exchange Microservices \& CI/CD Automation, Built from Zero}{\textcolor{black!60}{Sep 2018 -- Oct 2019}}{Research Software Engineer, Fraunhofer-Center for Maritime Logistics CML}{Hamburg, Germany}
+\item Set up the team's first Jenkins \textbf{CI/CD} pipeline with quality gates independently, bringing build automation to the group; also developed the SCEDAS crew-scheduling system (C\#, .NET, MS SQL, Entity Framework).
+\item Built containerized microservices (Express.js, \textbf{Docker}, SQLite) for MISSION, a Fraunhofer maritime data-exchange platform connecting ports, operators and research partners across the logistics chain.
+\end{rSubsection}
+
+% --- Vizrt (Jul 2017 -- May 2018) — VZ-1, VZ-2 ---
+\begin{rSubsection}{Distributed Real-Time Backend Engineering at Broadcast Scale}{\textcolor{black!60}{Jul 2017 -- May 2018}}{DevOps Engineer, Vizrt}{Bergen, Norway}
+\item Engineered distributed real-time video-transcoding backend components in \textbf{Python} (with legacy C++ modules) for Vizrt's broadcast platform, serving global media customers including CNN, BBC and Al Jazeera.
+\item Wrote an automated A/V integration and unit test suite in \textbf{Python} and wired quality gates into the \textbf{CI/CD} pipeline, which shortened the feedback loop for new features and raised release reliability.
+\end{rSubsection}
+
+% --- Generali (May 2015 -- Jun 2017) — GN-1, GN-3, GN-2 ---
+\begin{rSubsection}{Test Automation, CI/CD Ownership \& Java Backend}{\textcolor{black!60}{May 2015 -- Jun 2017}}{IT Consultant, Generali Deutschland Informatik Services}{Hamburg, Germany}
+\item Introduced BDD test automation to Generali (Serenity-BDD, Selenium, JBehave), ran the PoC and took technical ownership, administered Jenkins \textbf{CI/CD} jobs, and trained teams across the Java Community.
+\item Developed Java/J2EE features for the PIA-Postkorb workflow portal, migrated WebServices to the XLDeploy process, and contributed to an Apache Camel / Spring Boot dispatcher integration PoC.
+\item Pioneered UIPath RPA at Generali GDIS, building PoCs and serving as the internal RPA contact for group companies, extending automation from test tooling into broader business process automation.
+\end{rSubsection}
+
+
+\end{rSection}
+\vspace{-0.15cm}
+
+%----------------------------------------------------------------------------------------
+%	EDUCATION — FIXED
+%----------------------------------------------------------------------------------------
+\begin{rSection}{Education}
+{M.Eng.\ Computer Aided Engineering (Software Design \& Engineering)} \hfill {\textcolor{black!60}{Apr 2012 -- Oct 2013}}\\
+{Universit\"at der Bundeswehr M\"unchen}; thesis at Tongji University, Shanghai \hfill Thesis Grade: \textbf{1.0}\\
+{\small Thesis: \textit{Development of a Web-Based Remote Fault Diagnosis System} (Neural Networks, PSO, Fuzzy Logic)}
+
+{B.Eng.\ Information and Telecommunication Technologies} \hfill {\textcolor{black!60}{Oct 2009 -- Oct 2012}}\\
+{Universit\"at der Bundeswehr M\"unchen}, Munich, Germany
+\end{rSection}
+\vspace{-0.15cm}
+
+%----------------------------------------------------------------------------------------
+%	CERTIFICATIONS & AWARDS — FIXED
+%----------------------------------------------------------------------------------------
+\begin{rSection2}{Certifications \& Awards}
+\item \textbf{AWS Certified Solutions Architect -- Associate}, Amazon Web Services (2024, active until Sep 2027).
+\item \textbf{Data Engineering with AWS Nanodegree}, Udacity (2026). AWS data pipeline architecture.
+\item \textbf{IBM AI Engineering Specialization}, Coursera. Deep learning, TensorFlow, Keras, Apache Spark ML.
+\item \textbf{iSAQB CPSA -- Foundation Level}, iSAQB (2016). Certified Professional for Software Architecture.
+\item \textbf{ITIL Foundation Certificate in IT Service Management}, PEOPLECERT / AXELOS (2016).
+\end{rSection2}
+
+\begin{center}
+\vspace{0.1cm}
+\textit{Languages: German (native), English (fluent)}
+\end{center}
+
+\end{document}

output/Google_Senior_Data_Engineer/resume.cls

@@ -0,0 +1,199 @@
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Medium Length Professional CV - RESUME CLASS FILE
+%
+% This template has been downloaded from:
+% http://www.LaTeXTemplates.com
+%
+% This class file defines the structure and design of the template. 
+%
+% Original header:
+% Copyright (C) 2010 by Trey Hunner
+%
+% Copying and distribution of this file, with or without modification,
+% are permitted in any medium without royalty provided the copyright
+% notice and this notice are preserved. This file is offered as-is,
+% without any warranty.
+%
+% Created by Trey Hunner and modified by www.LaTeXTemplates.com
+%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+\ProvidesClass{resume}[2018/09/25 v1.0 Resume class]  
+
+\LoadClass[10pt, a4paper]{article} % Font size and paper type
+\usepackage{lastpage}
+\usepackage[parfill]{parskip} % Remove paragraph indentation
+\usepackage{array} % Required for boldface (\bf and \bfseries) tabular columns
+\usepackage{ifthen} % Required for ifthenelse statements
+\usepackage{enumitem}
+ \pagestyle{empty} % Suppress page numbers
+
+%----------------------------------------------------------------------------------------
+%	HEADINGS COMMANDS: Commands for printing name and address
+%----------------------------------------------------------------------------------------
+
+\def \name#1{\def\@name{#1}} % Defines the \name command to set name
+\def \@name {} % Sets \@name to empty by default
+
+\def \addressSep {$|$} % Set default address separator to a diamond
+
+% One, two or three address lines can be specified 
+\let \@addressone \relax
+\let \@addresstwo \relax
+\let \@addressthree \relax
+\let \@addressfour \relax
+
+% \address command can be used to set the first, second, and third address (last 2 optional)
+\def \address #1{
+  \@ifundefined{@addresstwo}{
+    \def \@addresstwo {#1}
+  }{
+  \@ifundefined{@addressthree}{
+  \def \@addressthree {#1}
+  }{
+  \@ifundefined{@addressfour}{
+  \def \@addressfour {#1}
+  } {\def \@addressone {#1}
+  }
+     
+  }
+  }
+}
+
+% \printaddress is used to style an address line (given as input)
+\def \printaddress #1{
+  \begingroup
+    \def \\ {\addressSep\ }
+             {#1}
+%    \centerline{#1}
+  \endgroup
+  \par
+ % \addressskip
+}
+
+% \printname is used to print the name as a page header
+\def \printname {
+  \begingroup
+  % \MakeUppercase
+  {\namesize\bf \@name} \hfil
+%    \hfil{\MakeUppercase{\namesize\bf \@name}}\hfil
+    \nameskip\break
+  \endgroup
+}
+
+%----------------------------------------------------------------------------------------
+%	PRINT THE HEADING LINES
+%----------------------------------------------------------------------------------------
+
+\let\ori@document=\document
+\renewcommand{\document}{
+  \ori@document  % Begin document
+  % \begin{center}
+  \printname % Print the name specified with \name
+  \@ifundefined{@addressone}{}{ % Print the first address if specified
+    \printaddress{\@addressone}}
+  \@ifundefined{@addresstwo}{}{ % Print the second address if specified
+    \printaddress{\@addresstwo}}
+     \@ifundefined{@addressthree}{}{ % Print the third address if specified
+    \printaddress{\@addressthree}}
+     \@ifundefined{@addressfour}{}{ % Print the third address if specified
+    \printaddress{\@addressfour}}
+    
+    % \end{center}
+}
+
+%----------------------------------------------------------------------------------------
+%	SECTION FORMATTING
+%----------------------------------------------------------------------------------------
+
+% Defines the rSection environment for the large sections within the CV
+\newenvironment{rSection}[1]{ % 1 input argument - section name
+   \sectionskip
+   {\bf #1}
+%   \MakeUppercase{\bf #1} % Section title
+   \sectionlineskip
+  \hrule % Horizontal line
+  \begin{list}{}{ % List for each individual item in the section
+    \setlength{\leftmargin}{0.50em} % Margin within the section
+  }
+  \item[]
+}{
+  \end{list}
+}
+
+\newenvironment{rSection2}[1]{ % 1 input argument - section name
+  \sectionskip
+  {\bf #1} % Section title
+  \sectionlineskip
+  \hrule % Horizontal line
+  \medskip
+  \begin{list}{$\bullet$}{\setlength{\leftmargin}{1.5em}}
+   \itemsep -0.3em \vspace{-0.5em} % Compress items in list together for aesthetics
+}{
+  \end{list}
+ \vspace{0.5em}
+}
+
+\newenvironment{rSection3}[1]{ % 1 input argument - section name
+  \sectionskip
+  {\bf #1} % Section title
+  \sectionlineskip
+  \hrule % Horizontal line
+  \medskip
+  \begin{enumerate}[]{\setlength{\leftmargin}{1.5em}}
+   \itemsep -0.3em \vspace{-0.5em} % Compress items in list together for aesthetics
+}{
+  \end{enumerate}
+ \vspace{0.5em}
+}
+%----------------------------------------------------------------------------------------
+%	WORK EXPERIENCE FORMATTING
+%----------------------------------------------------------------------------------------
+
+\newenvironment{rSubsection}[4]{ % 4 input arguments - company name, year(s) employed, job title and location
+ {\bf #1} \hfill {#2} % Bold company name and date on the right
+ \ifthenelse{\equal{#3}{}}{}{ % If the third argument is not specified, don't print the job title and location line
+  \\
+  {\em #3} \quad {\em #4} % Italic job title and location
+  }\smallskip
+  \begin{list}{$\cdot$}{\leftmargin=1.5em} % \cdot used for bullets, no indentation
+   \itemsep -0.2em \vspace{-0.2em} % Compress items in list together for aesthetics
+  }{
+  \end{list}
+  \vspace{0.2 em} % Some space after the list of bullet points
+}
+
+
+
+%----------------------------------------------------------------------------------------
+%	FORMAT C SKILLS COMMANDS
+%----------------------------------------------------------------------------------------
+
+% Skills group environment: \begin{skillgroup}{Group Name} ... \end{skillgroup}
+% Renders bold header + indented dash sub-items. Each \skilldash = exactly 1 rendered line.
+\newenvironment{skillgroup}[1]{%
+  \textbf{#1}\par\nopagebreak%
+  \vspace{-\parskip}%
+  \begin{list}{--}{\leftmargin=0.8em \labelsep=0.3em \itemsep=0pt \topsep=0.1em \parsep=0pt \partopsep=0pt}%
+}{%
+  \end{list}%
+  \vspace{-\parskip}\vspace{0.45em}%
+}
+
+% Single dash sub-item within a skillgroup. Content must fit 1 rendered line.
+% Char limit: 119 - (0.5 x bold_char_count) at 10pt
+\newcommand{\skilldash}[1]{\item #1}
+
+%----------------------------------------------------------------------------------------
+%	EXPERIENCE SUB-THEME COMMAND
+%----------------------------------------------------------------------------------------
+
+% Sub-theme underline header within rSubsection
+\newcommand{\subtheme}[1]{\item[] \underline{#1}}
+
+% The below commands define the whitespace after certain things in the document - they can be \smallskip, \medskip or \bigskip
+\def\namesize{\huge} % Size of the name at the top of the document
+\def\addressskip{\smallskip} % The space between the two address (or phone/email) lines
+\def\sectionlineskip{\medskip} % The space above the horizontal line for each section 
+\def\nameskip{\medskip} % The space after your name at the top
+\def\sectionskip{\medskip} % The space after the heading section

output/Google_Senior_Data_Engineer/session_google_senior_data_engineer.md

@@ -0,0 +1,134 @@
+# Session: Google — Senior Data Engineer (Merchant Data Science)
+
+## JD Info
+- **File:** JDs/google_senior_data_engineer.txt
+- **JD source:** live scrape 2026-06-15 via Playwright (Google careers board), re-verified live same day
+- **URL:** https://www.google.com/about/careers/applications/jobs/results/87066954308690630-senior-data-engineer?location=Switzerland
+- **Role:** Senior Data Engineer — Merchant Data Science team, Merchant Shopping Organization
+- **Company:** Google (Alphabet)
+- **Bundle:** Staff / Senior Data Engineer (primary, Tier 1) + Analytics Engineer (secondary bridge — BI/self-serve/stakeholder)
+- **Format:** Resume (2-page, resume.cls) + 1-page cover letter. NON-USA target (Zürich) → omit US immigration line.
+- **Level/Comp:** Level chip "Mid" despite "Senior" title — clarify L4 vs L5 at recruiter stage. US band shown $156–227k + 15% + equity. Zürich (Levels.fyi): L4 median ~CHF 240k total, L5 ~CHF 293k. **Even L4 clears the 180k+ all-in bar.**
+
+## JD Analysis
+### Requirements
+| # | Requirement | Match | Evidence |
+|---|-------------|-------|----------|
+| 1 | Bachelor's / equivalent practical experience | Direct | M.Eng. Computer Aided Engineering (thesis grade 1.0) |
+| 2 | 5+ yrs designing data pipelines + dimensional data modeling, sync/async integration, internal (Flume) + external (DataFlow, Spark) stacks | Direct (tool-bridge on Flume/DataFlow) | SW-1 (AWS Glue/Airflow/Redshift ETL), SW-6 (PySpark/Spark), SW-4 (Oracle/Kafka→Teradata), BS-4. Spark direct; Flume/DataFlow NOT used — bridge as transferable distributed processing, never claim |
+| 3 | 5+ yrs coding in 1+ languages | Direct | Python (expert, 10+ yrs), Java, SQL, C# |
+| 4 | 5+ yrs data infrastructure + data models, exploratory queries/scripts | Direct | SW data platform (S3/Glue/Athena/Redshift), Data Lake, SQL, data products/modeling |
+| 5 (pref) | 5+ yrs statistical methodology + BI/data-consumption tools (Tableau, Power BI, DataStudio, Jupyter, collabs) | Bridge (MED) | Dashboards for B2B teams (SW-4); Jupyter/Pandas likely; named BI tools (Tableau/PowerBI) thin — verify in experience files, don't oversell |
+| 6 (pref) | 3+ yrs project plans + on-time/in-budget/in-scope delivery | Direct | Component Owner (SW-2), Application Owner (BS-3), delivery ownership |
+| 7 (pref) | 3+ yrs stakeholder partnering/management | Direct | SW-4 (PO/stakeholder, B2B product teams), BS-3 (vendors, cross-team) |
+| 8 (pref) | ML for production workflows | Direct | BS-1 production ML inference in 24/7 fab |
+| RESP | Advanced data engineering, data modeling, architectural frameworks | Direct | Data Mesh data products, data modeling, AWS architecture (iSAQB) |
+| RESP | Design/build/scale data products incl self-serve tools + automated pipelines | **Direct — near verbatim** | SW-7 self-service governed data products consumed by downstream teams |
+| RESP | Data infra, product quality, automated validation, data quality, reliability monitoring | Direct | SW data governance/SLAs, BS-4 observability/anomaly detection/monitoring |
+| RESP | High autonomy, own projects conception→landing→impact | Direct | Component Owner + Application Owner titles; FC-1 built CI/CD from zero |
+| RESP | Engineering excellence, robust data, sharp communication | Direct | Training/docs (BS-3), cross-team adoption, RCA |
+
+### ATS Keywords
+- **Core DE:** data pipelines, dimensional data modeling, data products, self-serve tools, automated pipelines, data infrastructure, data models, ETL/ELT, data warehouse, distributed data processing
+- **Tools:** Spark (DataFlow/Flume — bridge, never claim), Airflow, SQL, Python, BigQuery (bridge via Athena/Redshift/Iceberg — do NOT claim by name), Glue, Kafka
+- **Quality/reliability:** data quality, automated validation frameworks, reliability monitoring, data governance, SLAs, observability
+- **Collaboration:** stakeholder management, cross-functional, bridge SWE/DE/DS, data-driven decision-making, multidisciplinary
+- **Preferred:** statistical methodology, BI tools (see BI-tool note below), ML for production workflows, project delivery
+
+### BI / Data-Consumption Tools (user-confirmed 2026-06-15 — usable, honest leveling)
+- **TIBCO Spotfire (Bosch) — CO-OWNED platform + extended in C# + TAF 2022 speaker (VERIFIED).** Co-owned the Spotfire analytics platform serving fab engineers (+ Defect Management System); built C# extensions. **Co-presented at TIBCO Analytics Forum 2022** ("Understanding Spatial Variability in Semiconductor Products with Spotfire Map Charts"). Verified via community.spotfire.com scrape 2026-06-15. See [[taf_2022_spotfire]] + experience_bosch.md BS-5. Use TAF talk as CL hook + interview bridge.
+- **Tableau (Swisscom)** — legacy DWH reporting; list as named tool at exposure weight, do NOT claim proficiency.
+- **AWS QuickSight (Swisscom)** — new reporting; BASIC knowledge only. Mention lightly / fold under AWS; do not oversell.
+- **Jupyter / Pandas** — covers the "collabs/notebooks" part of the qual.
+- **Soft:** high autonomy/ownership, engineering excellence, sharp communication
+
+### Gap Assessment
+- **Direct:** data pipeline design, dimensional modeling, Spark, SQL, Python, data infrastructure, self-serve data products (flagship), data quality/validation, reliability monitoring, stakeholder mgmt, project delivery, production ML, autonomy/ownership
+- **Bridge:** Flume/DataFlow/BigQuery (Google-internal/GCP — bridge via Spark/Airflow/Glue/Athena/Redshift/Iceberg, transferable, NEVER claim by name); named BI tools Tableau/PowerBI/DataStudio (MED — has dashboards/Jupyter, verify before claiming); statistical methodology (MED — has ML/analytics, don't oversell)
+- **Gap (do NOT claim):** GCP/BigQuery/Dataflow hands-on, Google-scale data, e-commerce/marketplace domain. Honest gaps. Google explicitly accepts "equivalent practical experience"; he meets every minimum qual.
+
+## Company Context
+- **Mission:** Make Google the best place for merchants to connect with shoppers; the **Shopping Graph** (60B+ products indexed) is central. Merchant Shopping Org builds the data foundation behind Shopping + AI experiences (Gemini, AIM).
+- **This team:** **Merchant Data Science** — global team with members in US, London, **Zürich**. Builds scalable data products that empower data-driven decision-making; partners with PMs, engineers, data scientists. This role explicitly "bridges the gap between software engineering, data engineering, and data science."
+- **Google data stack (context):** Flume/FlumeJava, Dataflow, BigQuery (Dremel/Capacitor/Colossus/Borg), ZetaSQL, Spanner. JD names Flume + DataFlow. Dennis's AWS/Spark/Airflow analog is conceptually transferable (distributed processing, columnar warehouses, SQL-on-data-lake).
+- **Culture:** Data-product engineering excellence, autonomy, cross-functional, scale. Equivalent-practical-experience accepted.
+- **"Why them" angle:** Dennis already builds self-service, governed data products consumed across teams at telco scale — the exact "durable data products that bridge SWE/DE/DS" this team builds. Add production-ML + data-quality/reliability discipline. Zürich team = local to Bern.
+
+## Framing Strategy
+- **Lead narrative:** "Data engineer who designs and owns self-service, governed data products and the pipelines/dimensional models behind them — built for downstream teams to make data-driven decisions, with production-ML and data-quality discipline on top."
+- **Reframing map:**
+  - SW-7 Data Mesh self-serve data products → "durable, self-serve data products consumed across the org" (the JD's core ask)
+  - SW-1 Teradata/Oracle→AWS ETL + Glue/Airflow/Redshift → "data pipeline design + dimensional modeling on a cloud data platform"
+  - SW-6 PySpark/Data Lake → "distributed data processing (Spark; transferable to DataFlow/Flume)"
+  - BS-4 ELK/Grafana/Prometheus + anomaly detection → "automated validation, data quality + reliability monitoring"
+  - SW-4 dashboards + PO/stakeholder → "data products for stakeholders + cross-functional partnering" (Analytics-Eng bridge)
+  - BS-1 production ML → "ML for production workflows" (preferred qual)
+  - Component/Application Owner → "high autonomy, own projects conception→impact"
+- **Emphasize:** self-serve data products (flagship), pipeline design + dimensional modeling, Spark, data quality/validation/reliability monitoring, stakeholder partnering, autonomy, production ML
+- **Downplay:** SRE/on-call framing (less central here than Kraken), crypto/Web3 (NOT relevant — drop crypto skills group), semiconductor jargon (keep ML + data-quality angle), pure DevOps/K8s emphasis (secondary here)
+- **CL hooks:** (1) Shopping Graph as a data product at 60B-product scale ↔ his self-serve governed data products; (2) "bridge SWE/DE/DS" = his cross-functional Component/App Owner role; (3) data quality + reliability monitoring (his observability/governance work) for AI-powered Shopping experiences; (4) Zürich Merchant Data Science team, local to Bern.
+- **User directives:** Honest tool-bridging (NO BigQuery/Dataflow/Flume by name). Clarify level/comp at recruiter stage (comp clears bar even at L4). Drop crypto framing (not relevant to this JD).
+
+## Critique Context
+- **Reviewer persona:** A Google L5/L6 Data Engineer tech lead or DE manager on Merchant Data Science (Zürich). Cares about real pipeline + dimensional-modeling depth, data-product thinking (self-serve, durable, consumed at scale), data-quality/validation rigor, autonomy, and Google-scale readiness. Bored by buzzwords and BI-dashboard-only profiles. Probes whether candidate has designed data models, not just moved data.
+- **Competitive landscape:** Obvious fit = DE with BigQuery/Dataflow/dbt + e-commerce/marketplace data, possibly ex-FAANG, fluent in dimensional modeling at scale. Dennis's edge: self-serve data-product/Data-Mesh platform thinking at telco scale, production ML, data-quality/reliability discipline, and broad multi-industry delivery. Their edge: GCP/BigQuery by name, Google-scale, marketplace domain.
+- **Domain vocabulary (insider):** Shopping Graph, data products, self-serve, dimensional modeling, fact/dimension tables, pipeline DAG, data quality SLAs, validation frameworks, freshness/completeness, lineage, Flume/Dataflow/BigQuery, Dremel/ZetaSQL, batch + streaming, idempotency, backfill.
+
+## Cover Letter Plan
+- **Institution type:** Industry (big-tech, data-engineering audience)
+- **Paragraph count:** 3-4 paragraphs, ~270 words
+- **P1 hook:** Shopping Graph as a 60B-product data product + Merchant Data Science "bridge SWE/DE/DS" ↔ my self-serve governed data products at telco scale; Zürich team local fit
+- **P2-P3 evidence:** P2 = pipeline design + dimensional modeling + self-serve data products (SW-1, SW-7, SW-6) consumed across teams. P3 = data quality/validation + reliability monitoring (BS-4) + production ML (BS-1) + cross-functional stakeholder ownership (SW-4, BS-3)
+- **Domain pivot:** AWS/Spark/Airflow honestly transferable to Google's Flume/DataFlow/BigQuery — state the analog confidently, never claim the Google-internal tools by name
+- **Jargon level:** Technical (DE audience)
+- **"Why them" hook:** Builds data products others depend on to decide — wants to do it at Shopping-Graph scale on the Zürich team
+
+## Bullet Plan (proposed 2026-06-15 — 17 variable bullets, proven QuantCo 2-page fill)
+
+Drop SW-5 (security — not relevant, avoids "3 years" correction). Drop crypto skills group → replace with BI/Analytics group. No US immigration line (Zürich target). TAF 2022 Spotfire talk → CL hook + interview bridge (Option A) unless user wants its own bullet (Option B).
+
+### Position 1 — Swisscom · Staff Data, Analytics & AI Engineer (6 bullets)
+| # | ID | Achievement | Variant |
+|---|----|------------|---------|
+| 1 | SW-7 | LEAD — self-serve governed data products + metadata on AWS Data Mesh, consumed across teams | 2L |
+| 2 | SW-1 | AWS migration of Teradata/Oracle ETL + dimensional modeling (Glue/Athena/Iceberg/Redshift/Airflow) | 2L |
+| 3 | SW-2 | Component Owner Fulfillment ETL (Oracle/Kafka→Teradata), data governance + quality + SLA | 2L |
+| 4 | SW-4 | B2B data products + dashboards (Tableau/QuickSight) + stakeholder + automation + RCA | 2L |
+| 5 | SW-6 | PySpark distributed data processing in Data Lake | 2L |
+| 6 | SW-3 | Python apps on Kubernetes + GitLab CI/CD (engineering credibility) | 2L |
+
+### Position 2 — Bosch · (Senior) Data Engineer (4 bullets)
+| # | ID | Achievement | Variant |
+|---|----|------------|---------|
+| 1 | BS-1 | Production ML inference into 24/7 fab (Docker/K8s/Ansible) — "ML for production workflows" | 2L |
+| 2 | BS-3+BS-5 | Application Owner + CO-OWNED TIBCO Spotfire analytics platform + Defect Mgmt System; C# extensions; SLOs, training | 2L |
+| 3 | BS-4 | ELK/Grafana/Prometheus anomaly detection — data quality + reliability monitoring | 2L |
+| 4 | BS-2 | Multi-language data services over Oracle + Hadoop/Impala for analysis teams | 2L |
+
+### Position 3 — Fraunhofer (2) · Position 4 — Vizrt (2) · Position 5 — Generali (3)
+FC-1 (Jenkins CI/CD from zero + SCEDAS), FC-3 (Express.js/Docker microservices) | VZ-1 (distributed Python/C++ backend, CNN/BBC), VZ-2 (test suite + CI/CD gates) | GN-1 (BDD + Jenkins ownership), GN-3 (Java/J2EE), GN-2 (UIPath RPA)
+
+**Skills (4-3-2-2-2):** (1) Data Engineering & Pipelines, (2) Cloud Platform & Infrastructure, (3) BI/Analytics & Data Consumption (Spotfire/Tableau/QuickSight/Jupyter), (4) Programming & Databases, (5) Certifications.
+**Summary headline:** "Senior Data Engineer | Python · SQL · AWS · Spark | Data Products, Dimensional Modeling & Pipeline Ownership"
+
+**TAF 2022 decision (pending user):** Option A = combine BS-3+BS-5 (1 bullet) + TAF talk in CL/interview (RECOMMENDED, keeps DE focus, 17 bullets). Option B = Spotfire+TAF its own bullet (Bosch→5), drop GN-2 to stay at 17.
+
+## Output Files
+- Resume: `output/Google_Senior_Data_Engineer/e2e_google_senior_data_engineer_resume.tex` (+ .pdf, 2 pages)
+- Cover Letter: `output/Google_Senior_Data_Engineer/e2e_google_senior_data_engineer_cover_letter.tex` (+ .pdf, 1 page, 299 words). Hooks verified 2026-06-15: (1) Shopping Graph 60B+ listings — confirmed (Google I/O 19 May 2026, VP Vidhya Srinivasan); (2) TAF 2022 talk "Understanding Spatial Variability in Semiconductor Products with Spotfire Map Charts" by Mark Herrmann + Dennis Thießen (Bosch Dresden) — confirmed (community.spotfire.com/articles/spotfire/tibco-analytics-forum-2022). NOTE: moderncv needs `\microtypesetup{expansion=false}` or fontawesome icons fail compile.
+- Critique: CURRENT — **85.5/100** (2026-06-15; baseline 83.0 pre-edit). Strong Tier-1 DE fit; SW-7 self-serve data products ≈ team charter verbatim; honest GCP-tool bridges; AI scan clean; CL 1pp. **Tier-1 + both Tier-2 fixes APPLIED & re-verified:** (1) migration claim re-scoped in resume B2 + CL P2 (Scope-Discipline error cleared in both docs); (2) B4 now reads "on time and in scope" (project-delivery preferred qual); (3) B5 "distributed computing"→"distributed data processing". Resume 2pp / CL 1pp clean compile, B2 216/B4 190/B5 206 chars (≤218). **SENT 2026-06-15.** Hard ceiling ~87 (no GCP/BigQuery-by-name, no marketplace domain — not closable). Open item at recruiter stage: clarify L4/L5 + confirm comp clears 180k+.
+
+## Status
+- Phase 0: DONE
+- Phase 1: DONE (17 bullets confirmed; Option A — TAF talk reserved for CL, not on resume; IBM AI Engineering kept in awards)
+- Phase 2 Resume: DONE (2 pages, MiKTeX, all bullets in char range, summary 525 chars, clean compile). Header tagline = Senior Data Engineer; BI/Analytics group added; crypto group dropped; no immigration line. SW-7 lead = data products; BS-3 = Spotfire platform co-ownership + C# extensions.
+- Cover Letter: DONE (1 page, 299 words, 3 paragraphs, clean MiKTeX compile, both hooks verified, anti-pattern scan clean — 0 em-dashes)
+- Critique: PENDING
+- **Next CL:** DONE — see Output Files
+- **Next Critique:** /critique output/Google_Senior_Data_Engineer/session_google_senior_data_engineer.md
+- Phase 2 Resume: PENDING
+- Cover Letter: PENDING
+- Critique: PENDING
+- **Next:** Phase 1 — bullet plan (this session)
+- **Next CL:** /make-cl output/Google_Senior_Data_Engineer/session_google_senior_data_engineer.md
+- **Next Critique:** /critique output/Google_Senior_Data_Engineer/session_google_senior_data_engineer.md